You are working as a Machine Learning Engineer and have designed an ML pipeline with multiple input parameters. Your main goal is to evaluate the tradeoffs between different combinations of the following parameters: Input dataset, Max tree depth of the boosted tree regressor, and Optimizer learning rate. To facilitate this, you need to compare the pipeline performance of these different parameter combinations based on F1 score, training time, and model complexity. Additionally, you want your approach to be reproducible and ensure that all pipeline runs are tracked on the same platform. What should you do?